5 Key Areas Shaping the Future of GenAI and Cloud Native Transformation

With 2024 in the rearview, the seventh annual Nutanix Enterprise Cloud Index (ECI) reveals how GenAI and cloud native technologies are fundamentally reshaping enterprise IT.

Drawing from a global survey of 1,500 IT, DevOps and platform engineering decision-makers, including those at the C-level, the report highlights five key areas where GenAI is driving the next wave of digital transformation: application containerization, GenAI adoption, data security, modernization to support GenAI scaling, and talent development.

Let’s first dive into why 98% of ECI respondents say they are at least in the process of containerizing apps—and why only being in the process won’t be enough with the rapid adoption of new workloads like GenAI.

1. The Rise of Application Containerization

Containerization, cloud native applications and AI solution development have all become closely intertwined, and the ECI report reflects this shift. Nearly 90% of ECI organizations report that at least some of their apps are now containerized.

Containerization also streamlines app deployment, simplifies scaling and enhances security, making it the gold standard for modern IT infrastructure and the preferred method for deploying GenAI. In fact, 70% of respondents in this year’s survey say they will containerize their GenAI applications, the highest among all app categories.

However, 80% of organizations said their current IT infrastructure requires some level of improvement to support cloud native applications and containers. 

2. The Rapid Adoption of GenAI

It’s not just buzz. GenAI is quickly becoming the backbone of digital transformation, empowering businesses to improve internal productivity and build more engaging customer experiences.

According to the ECI report, 85% of organizations already have a GenAI strategy in place, with only 2% reporting that they have not yet started planning. The prevalence of GenAI strategies highlights the urgent need to leverage AI models to improve decision-making, automate repetitive tasks and drive innovation.

But what are businesses using GenAI for? The use cases are diverse and growing:

• 53% are using GenAI for customer support and experience solutions.
• 46% leverage it for automated content generation. 
• 47% are tapping into GenAI for cybersecurity, fraud detection and loss prevention.
  

With its rapid adoption, GenAI is becoming a central piece of innovation. But protecting both GenAI models and data presents its own set of challenges, particularly for organizations that require a GenAI environment capable of meeting enterprise resilience, day 2 operations, and compliance requirements.

3. The Security and Privacy Challenge

As transformative as GenAI is, it brings with it the pressing need for robust data security and privacy. The ECI report found that data privacy and security is the top concern for GenAI adoption, with 30% of respondents ranking it as the most important aspect of deployment, followed by performance (23%) and scalability (22%). 

Despite these concerns, 95% of organizations believe they could still do more to secure their GenAI models. 38% of respondents are concerned about privacy and security risks related to using large language models (LLMs) with sensitive data. Additionally, 31% highlighted the complexity of building a secure GenAI environment from scratch.

Luckily, organizations are investing in the right areas to address these gaps. Half of all respondents identify the need to invest in cybersecurity and data governance, and 53% see GenAI as an opportunity to upskill their teams—transforming them into AI experts over time.

As businesses scale GenAI, maintaining robust security while protecting sensitive data will remain a critical investment. The companies that invest in both modern infrastructure and expertise will be better positioned to emerge as the leaders in this new era of AI-driven business.

4. Infrastructure Modernization to Support GenAI at Scale

Scaling GenAI workloads is not a straightforward journey, particularly in integrating existing IT systems with the specialized demands of GenAI. According to this year’s survey, an astounding 98% of respondents face challenges when it comes to scaling GenAI workloads from development to production. In fact, the number one challenge organizations face when scaling GenAI workloads from development into production is integration with existing IT infrastructure (54%).

This is likely tied to the fact that infrastructure required for GenAI workloads is more demanding than that required for traditional enterprise workloads. GenAI workloads require high-performance computing, high-throughput storage and low-latency networking—all while maintaining security and data integrity.

Managing the lifecycle of GenAI models presents additional hurdles. While 79% of organizations plan to implement processes for managing the lifecycle of GenAI models from development to deployment, the reality is that many are still playing catch-up. Organizations are still in the planning phase when it comes to managing GenAI lifecycles, with 52% planning to leverage third-party MLOps platforms, 48% planning to build in-house developed tools, and 21% saying they don’t/will not have any processes or tools in place to manage the lifecycle of GenAI models.

A key ingredient to successfully scaling GenAI though are the DevOps and engineering teams that keep them up and running.

5.  The People Factor: Training and Talent for GenAI

The explosion of GenAI isn’t just a tech shift—it’s a workforce shift. The demand for skilled AI professionals is fierce, with 52% of organizations acknowledging the need for more IT training related to GenAI. Yet, only 48% are looking to hire new talent.

While the talent shortage is real, many organizations are finding solutions closer to home. 85% of businesses plan to purchase existing AI models or leverage existing open-source AI models in order to build their AI apps, bypassing the need to build models from scratch. Only 10% intend to build their own models, signaling ready-made, tunable solutions are the way forward for most.

On top of this, not all AI expertise needs to come from external hires. Many companies are finding that internal upskilling is a viable way to grow their GenAI competencies, with 53% of ECI respondents believing GenAI advancements will provide an opportunity for employees to become AI experts.

The bottom line? Talent shortages are not insurmountable. By focusing on upskilling existing teams, leveraging external solutions and strategically hiring, organizations can bridge the GenAI skill gap.

The Bottom Line

GenAI isn’t just a passing trend—it's the future of business. Its transformative impact on workloads, infrastructure and workforce strategies requires organizations to rethink everything. From cloud native architectures to security frameworks and talent management, GenAI demands a new approach to digital transformation.

Organizations that are serious about GenAI adoption will need to:

• Modernize IT Infrastructure to support scalable, containerized GenAI workloads
• Prioritize security and privacy for GenAI infrastructure
• Invest in talent through both upskilling and new hires

Ready to get started? Check out the full report for more insights on how to navigate the world of GenAI and cloud native transformation.